He, Yulan and Young, S.
Hidden vector state model for hierarchical semantic parsing.
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference, 1
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The paper presents a hidden vector state (HVS) model for hierarchical semantic parsing. The model associates each state of push-down automata with the state of an HMM. State transitions are factored into separate stack pop and push operations and then constrained to give a tractable search space. The result is a model which is complex enough to capture hierarchical structure but which can be trained automatically from unannotated data. Experiments have been conducted on ATIS-3 1993 and 1994 test sets. The results show that the HVS model outperforms a general finite state tagger (FST) by 19% to 32% in error reduction.
||HMM; error reduction; general finite state tagger; hidden vector state model; hierarchical semantic parsing; push-down automata; search space; speech recognition; spoken dialogue systems; stack pop operations; stack push operations
||Knowledge Media Institute
|Interdisciplinary Research Centre:
||Centre for Research in Computing (CRC)
||29 Mar 2011 10:31
||28 Oct 2012 07:46
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